Segmentation of shallow scratches image using an improved multi-scale line detection approach

Along with developing modern technology, the demands for optical element surface develop towards the characteristics of large scale and high precision. However, it is challenging to evaluate the surface defects since some shallow scratches in optical element surface images are usually characterized by low contrast and blurry outlines. This property makes the machine vision inspection extremely difficult. So, this paper proposes a novel multi-scale line detection method that can efficiently extract shallow scratches. Firstly, to decrease the influence of the surrounding region, a new multi-scale line detector combines all the responses at different scales by setting different weights for each scale. Then, based on the scratches features, we utilize morphological operations to get the full continuum of the scratches area. Experimental results show that our model can ideally extract the contours of shallow scratches that are very close to the optical microscope results observed by specialists.

[1]  Birendra Biswal,et al.  Robust retinal blood vessel segmentation using line detectors with multiple masks , 2018, IET Image Process..

[2]  M. Belić,et al.  Raman solitons in nanoscale optical waveguides, with metamaterials, having polynomial law non-linearity , 2016 .

[3]  Jianwu Dang,et al.  Real time detection system for rail surface defects based on machine vision , 2018, EURASIP Journal on Image and Video Processing.

[4]  David M. Aikens,et al.  Implementing ISO standard-compliant freeform component drawings , 2016 .

[5]  Yongjie Zhao,et al.  Intelligent assessment of subsurface cracks in optical glass generated in mechanical grinding process , 2018, Adv. Eng. Softw..

[6]  De Xu,et al.  Weak scratch detection and defect classification methods for a large-aperture optical element , 2017 .

[7]  De Xu,et al.  A Novel and Effective Surface Flaw Inspection Instrument for Large-Aperture Optical Elements , 2015, IEEE Transactions on Instrumentation and Measurement.

[8]  Jia Zhang,et al.  A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection , 2014, Comput. Medical Imaging Graph..

[9]  Yu-De Lin,et al.  Automatic defect inspection system of colour filters using Taguchi-based neural network , 2013 .

[10]  Mohd. Marzuki Mustafa,et al.  SEGMENTATION OF RETINAL BLOOD VESSELS BY TOP-HAT MULTI-SCALE DETECTION FOR OPTIC DISC REMOVAL , 2015 .

[11]  Luiz Carlos Rodrigues,et al.  Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering , 2017, Biomed. Signal Process. Control..

[12]  Yan Sun,et al.  Different CO 2 absorbents-modified SBA-15 sorbent for highly selective CO 2 capture , 2017 .

[13]  Tariq M. Khan,et al.  A generalized multi-scale line-detection method to boost retinal vessel segmentation sensitivity , 2018, Pattern Analysis and Applications.

[14]  Ying Tian,et al.  Steel Surface Defect Detection Using a New Haar–Weibull-Variance Model in Unsupervised Manner , 2017, IEEE Transactions on Instrumentation and Measurement.

[15]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[16]  Xuedong Chen,et al.  A Monte Carlo analysis of uncertainty in supporting assembly of large-aperture optical lenses , 2013 .

[17]  Wenyuan Wu,et al.  An Automatic Approach for Retinal Vessel Segmentation by Multi-Scale Morphology and Seed Point Tracking , 2018 .

[18]  L. J. Miguel,et al.  Laser welding defects detection in automotive industry based on radiation and spectroscopical measurements , 2010 .

[19]  Xiangjun Gao Retinal vessel segmentation using an improved multi-scale line detection , 2013 .

[20]  Jian Bai,et al.  Dark-field detection method of shallow scratches on the super-smooth optical surface based on the technology of adaptive smoothing and morphological differencing , 2017 .

[21]  R. Mohammadi,et al.  Acoustic Emission-Based Methodology to Evaluate Delamination Crack Growth Under Quasi-static and Fatigue Loading Conditions , 2018 .

[22]  Kotagiri Ramamohanarao,et al.  An effective retinal blood vessel segmentation method using multi-scale line detection , 2013, Pattern Recognit..

[23]  Design and analysis of modified version of double aperture speckle interferometer consisting of holographic optical element: Application to measurement of in plane displacement component , 2015 .

[24]  Jian Gao,et al.  Automatic surface defect detection for mobile phone screen glass based on machine vision , 2017, Appl. Soft Comput..

[25]  S. Khonina,et al.  Implementation of ordinary and extraordinary beams interference by application of diffractive optical elements , 2016 .

[26]  Weisi Lin,et al.  Saliency-Based Defect Detection in Industrial Images by Using Phase Spectrum , 2014, IEEE Transactions on Industrial Informatics.

[27]  Hao Liu,et al.  An effective on-line surface particles inspection instrument for large aperture optical element , 2017, Int. J. Autom. Comput..

[28]  Jie Lei,et al.  Scale insensitive and focus driven mobile screen defect detection in industry , 2018, Neurocomputing.

[29]  Qiang Wu,et al.  Automatic diabetic retinopathy diagnosis using adjustable ophthalmoscope and multi-scale line operator , 2017, Pervasive Mob. Comput..

[30]  Yanli Hou,et al.  Automatic Segmentation of Retinal Blood Vessels Based on Improved Multiscale Line Detection , 2014, J. Comput. Sci. Eng..

[31]  Satoshi Goto,et al.  A New Multiscale Line Detection Approach for Aerial Image with Complex Scene , 2006, APCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems.

[32]  Du-Ming Tsai,et al.  An independent component analysis-based filter design for defect detection in low-contrast surface images , 2006, Pattern Recognit..

[33]  Louay A. Eldada,et al.  Optical communication components , 2004 .

[34]  Shanghong Zhao,et al.  Multi-parties Controlled Dense Coding via Maximal Slice States and the Physical Realization Using the Optical Elements , 2018 .

[35]  Jianhui Wu,et al.  Sub-Pixel Level Defect Detection Based on Notch Filter and Image Registration , 2017, Int. J. Pattern Recognit. Artif. Intell..

[36]  Elisa Ricci,et al.  Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification , 2007, IEEE Transactions on Medical Imaging.

[37]  Vijander Singh,et al.  An efficient similarity measure approach for PCB surface defect detection , 2018, Pattern Analysis and Applications.

[38]  Gholamreza Moradi,et al.  Defect Detection of Industrial Radiography Images of Ammonia Pipes by a Sparse Coding Model , 2018 .

[39]  Feihong Yu,et al.  Automatic inspection system of surface defects on optical IR-CUT filter based on machine vision , 2014 .